SoroushVahidi/scidocs huggingface dataset:
O Bitcoin Where Art Thou? Insight into Large-Scale Transaction Graphs
(2016)

Bitcoin is a rising digital currency and exemplifies the growing need for systematically gathering and analyzing public transaction data sets such as the blockchain. However, the blockchain in its raw form is just a large ledger listing transfers of currency units between alphanumeric character strings, without revealing contextually relevant real-world information. In this demo, we present GraphSense, which is a solution that applies a graph-centric perspective on digital currency transactions. It allows users to explore transactions and follow the money flow, facilitates analytics by semantically enriching the transaction graph, supports path and graph pattern search, and guides analysts to anomalous data points. To deal with the growing volume and velocity of transaction data, we implemented our solution on a horizontally scalable data processing and analytics infrastructure. Given the ongoing digital transformation in financial services and technologies, we believe that our approach contributes to development of analytics solutions for digital currency ecosystems, which is relevant in fields such as financial analytics, law enforcement, or scientific research.
Layering concepts in anterior composite restorations.
(2001)

SUMMARY
With increasing patient demands for esthetic dentition, composite resin restorations enjoy great popularity due to excellent esthetics, acceptable longevity, and relatively low costs. Shading concepts used by manufacturers, however, may be confusing to many clinicians.


PURPOSE
To review and describe the current main shading concepts, evaluate their potential for creating natural esthetics, and provide guidelines for application.
Distribution Free Prediction Bands
(2012)

We study distribution free, nonparametric prediction bands with a special focus on their finite sample behavior. First we investigate and develop different notions of finite sample coverage guarantees. Then we give a new prediction band estimator by combining the idea of “conformal prediction” (Vovk et al., 2009) with nonparametric conditional density estimation. The proposed estimator, called COPS (Conformal Optimized Prediction Set), always has finite sample guarantee in a stronger sense than the original conformal prediction estimator. Under regularity conditions the estimator converges to an oracle band at a minimax optimal rate. A fast approximation algorithm and a data driven method for selecting the bandwidth are developed. The method is illustrated first in simulated data. Then, an application shows that the proposed method gives desirable prediction intervals in an automatic way, as compared to the classical linear regression modeling.
Harnessing the Influence of Social Proof in Online Shopping: The Effect of Electronic Word of Mouth on Sales of Digital Microproducts
(2011)

Social commerce has taken the e‐tailing world by storm. Business‐to‐consumer sites and, more important, intermediaries that facilitate shopping experience, continue to offer more and more innovative technologies to support social interaction among like‐ minded community members or friends who share the same shopping interests. Among these technologies, reviews, ratings, and recommendation systems have become some of the most popular social shopping platforms due to their ease of use and simplicity in sharing buying experience and aggregating evaluations. This paper studies the effect of electronic word of mouth (eWOM) communication among a closed community of book readers. We studied the entire market of Amazon Shorts e‐books, which are digital mi‐ croproducts sold at a low and uniform price. With the minimal role of price in the buying decision, social discussion via eWOM becomes a collective signal of reputation, and ultimately a significant demand driver. Our empirical study suggests that eWOM can be used to convey the reputation of the product (e.g., the book), the reputation of the brand (i.e., the author), and the reputation of complementary goods (e.g., books in the same category). Until newer social shopping technologies gain acceptance, eWOM technolo‐ gies should be considered by both e‐tailers and shoppers as the first and perhaps primary source of social buying experience.
EARNING INDEPENDENT CAUSAL MECHANISMS
(2017)

Independent causal mechanisms are a central concept in the study of causality with implications for machine learning tasks. In this work we develop an algorithm to recover a set of (inverse) independent mechanisms relating a distribution transformed by the mechanisms to a reference distribution. The approach is fully unsupervised and based on a set of experts that compete for data to specialize and extract the mechanisms. We test and analyze the proposed method on a series of experiments based on image transformations. Each expert successfully maps a subset of the transformed data to the original domain, and the learned mechanisms generalize to other domains. We discuss implications for domain transfer and links to recent trends in generative modeling.
Convolutional Recurrent Deep Learning Model for Sentence Classification
(2018)

As the amount of unstructured text data that humanity produces overall and on the Internet grows, so does the need to intelligently to process it and extract different types of knowledge from it. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been applied to natural language processing systems with comparative, remarkable results. The CNN is a noble approach to extract higher level features that are invariant to local translation. However, it requires stacking multiple convolutional layers in order to capture long-term dependencies, due to the locality of the convolutional and pooling layers. In this paper, we describe a joint CNN and RNN framework to overcome this problem. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning network, then, the pre-trained parameters of the network are used to initialize the model. At a final stage, the proposed framework combines former information with a set of feature maps learned by a convolutional layer with long-term dependencies learned via long-short-term memory. Empirically, we show that our approach, with slight hyperparameter tuning and static vectors, achieves outstanding results on multiple sentiment analysis benchmarks. Our approach outperforms several existing approaches in term of accuracy; our results are also competitive with the state-of-the-art results on the Stanford Large Movie Review data set with 93.3% accuracy, and the Stanford Sentiment Treebank data set with 48.8% fine-grained and 89.2% binary accuracy, respectively. Our approach has a significant role in reducing the number of parameters and constructing the convolutional layer followed by the recurrent layer as a substitute for the pooling layer. Our results show that we were able to reduce the loss of detailed, local information and capture long-term dependencies with an efficient framework that has fewer parameters and a high level of performance.
Predicting The Next App That You Are Going To Use
(2015)

Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mobile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intelligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a prediction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the real-time spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered features and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use.
Osteology of Ischikauia steenackeri (Teleostei: Cypriniformes) with comments on its systematic position
(2010)

The lakeweed chub Ischikauia steenackeri is a medium-sized, herbivorous fish and the sole extant member of the genus Ischikauia, which is endemic to Lake Biwa and the Yodo River drainage, Japan. In order to clarify its systematic position, the skeletal anatomy of I. steenackeri is described and its relationships with related genera are discussed. The present data suggest the monophyly of Ischikauia and seven cultrine genera (Culter, Chanodichthys, Megalobrama, Sinibrama, Hemiculter, Toxabramis, and Anabarilius) based on a unique character, the metapterygoid elongated dorsally. Additionally, our data suggest that Ischikauia is closely related to Culter, Chanodichthys, Megalobrama, and Sinibrama. This relationship is supported by three synapomorphies that are common to them: a narrow third infraorbital, dorsal extension of the third supraneural, and a large quadrate foramen.
Feature Selection for Unsupervised Learning
(2004)

In this paper, we identify two issues involved in developing an automated feature subset selection algorithm for unlabeled data: the need for finding the number of clusters in conjunction with feature selection, and the need for normalizing the bias of feature selection criteria with respect to dimension. We explore the feature selection problem and these issues through FSSEM (Feature Subset Selection using Expectation-Maximization (EM) clustering) and through two different performance criteria for evaluating candidate feature subsets: scatter separability and maximum likelihood. We present proofs on the dimensionality biases of these feature criteria, and present a cross-projection normalization scheme that can be applied to any criterion to ameliorate these biases. Our experiments show the need for feature selection, the need for addressing these two issues, and the effectiveness of our proposed solutions.
Machine learning: a review of classification and combining techniques
(2006)

Supervised classification is one of the tasks most frequently carried out by so-called Intelligent Systems. Thus, a large number of techniques have been developed based on Artificial Intelligence (Logic-based techniques, Perceptron-based techniques) and Statistics (Bayesian Networks, Instance-based techniques). The goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various classification algorithms and the recent attempt for improving classification accuracy—ensembles of classifiers.
Radiomics: extracting more information from medical images using advanced feature analysis.
(2012)

Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory.
Reduction Techniques for Instance-Based Learning Algorithms
(2000)

Instance-based learning algorithms are often faced with the problem of deciding which instances to store for use during generalization. Storing too many instances can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce storage requirements in instance-based learning algorithms and other exemplar-based algorithms. Second, it proposes six additional reduction algorithms called DROP1–DROP5 and DEL (three of which were first described in Wilson & Martinez, 1997c, as RT1–RT3) that can be used to remove instances from the concept description. These algorithms and 10 algorithms from the survey are compared on 31 classification tasks. Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise.
Decision Tree Induction Based on Efficient Tree Restructuring
(1997)

The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications.
Automatic camera and range sensor calibration using a single shot
(2012)

As a core robotic and vision problem, camera and range sensor calibration have been researched intensely over the last decades. However, robotic research efforts still often get heavily delayed by the requirement of setting up a calibrated system consisting of multiple cameras and range measurement units. With regard to removing this burden, we present a toolbox with web interface for fully automatic camera-to-camera and camera-to-range calibration. Our system is easy to setup and recovers intrinsic and extrinsic camera parameters as well as the transformation between cameras and range sensors within one minute. In contrast to existing calibration approaches, which often require user intervention, the proposed method is robust to varying imaging conditions, fully automatic, and easy to use since a single image and range scan proves sufficient for most calibration scenarios. Experimentally, we demonstrate that the proposed checkerboard corner detector significantly outperforms current state-of-the-art. Furthermore, the proposed camera-to-range registration method is able to discover multiple solutions in the case of ambiguities. Experiments using a variety of sensors such as grayscale and color cameras, the Kinect 3D sensor and the Velodyne HDL-64 laser scanner show the robustness of our method in different indoor and outdoor settings and under various lighting conditions.
A taxonomy for multi-agent robotics
(1996)

A key di culty in the design of multi-agent robotic systems is the size and complexity of the space of possible designs. In order to make principled design decisions, an understanding of the many possible system con gurations is essential. To this end, we present a taxonomy that classi es multiagent systems according to communication, computational and other capabilities. We survey existing e orts involving multi-agent systems according to their positions in the taxonomy. We also present additional results concerning multi-agent systems, with the dual purposes of illustrating the usefulness of the taxonomy in simplifying discourse about robot collective properties, and also demonstrating that a collective can be demonstrably more powerful than a single unit of the collective.
A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents
(2007)

This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit
Lexical Analysis for the Webshell Attacks
(2016)

In this paper, we proposed a design considering for web application firewall system. Anyway, the website managing, web technology and hacker technology are vastly different from each other, whilst hacker techs are keep on moving forward, while company and government MIS are difficult of updating their information to the latest state due to working, they are often not updated and followed to the latest stuffs. And moreover, these newest techs are in generally needed to be figured out whether it matches the compatibility of the old versions, such as the recent PHP 7, many MIS are not capable to update their software to handle the variants of WAF bypass methods.
Tagging The Web: Building A Robust Web Tagger with Neural Network
(2014)

In this paper, we address the problem of web-domain POS tagging using a twophase approach. The first phase learns representations that capture regularities underlying web text. The representation is integrated as features into a neural network that serves as a scorer for an easy-first POS tagger. Parameters of the neural network are trained using guided learning in the second phase. Experiment on the SANCL 2012 shared task show that our approach achieves 93.15% average tagging accuracy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers.
A hardware CABAC encoder for HEVC
(2013)

This paper presents a hardware design of context-based adaptive binary arithmetic coding (CABAC) for the emerging High efficiency video coding (HEVC) standard. While aiming at higher compression efficiency, the CABAC in HEVC also invests a lot of effort in the pursuit of parallelism and reducing hardware cost. Simulation results show that our design processes 1.18 bins per cycle on average. It can work at 357 MHz with 48.940K gates targeting 0.13 μm CMOS process. This processing rate can support real-time encoding for all sequences under common test conditions of HEVC standard conforming to the main profile level 6.1 of main tier or main profile level 5.1 of high tier.
Cervical Cancer Diagnosis Using Random Forest Classifier With SMOTE and Feature Reduction Techniques
(2018)

Cervical cancer is the fourth most common malignant disease in women’s worldwide. In most cases, cervical cancer symptoms are not noticeable at its early stages. There are a lot of factors that increase the risk of developing cervical cancer like human papilloma virus, sexual transmitted diseases, and smoking. Identifying those factors and building a classification model to classify whether the cases are cervical cancer or not is a challenging research. This study aims at using cervical cancer risk factors to build classification model using Random Forest (RF) classification technique with the synthetic minority oversampling technique (SMOTE) and two feature reduction techniques recursive feature elimination and principle component analysis (PCA). Most medical data sets are often imbalanced because the number of patients is much less than the number of non-patients. Because of the imbalance of the used data set, SMOTE is used to solve this problem. The data set consists of 32 risk factors and four target variables: Hinselmann, Schiller, Cytology, and Biopsy. After comparing the results, we find that the combination of the random forest classification technique with SMOTE improve the classification performance.
Social media-induced technostress: Its impact on the job performance of it professionals and the moderating role of job characteristics
(2017)

Using social media during work hours for non-work-related reasons is becoming commonplace. Organizations are therefore challenged with identifying and overcoming the consequences of such use. Social media-induced technostress has been identified as an important unintended consequence of using social media at work, as it could negatively impact job performance. This study draws on Person-Environment Fit to investigate the relationship between social media-induced technostress and job performance in IT professionals, and the moderating effect of job characteristics on this relationship. The results indicate that social media-induced technostress is negatively related to job performance and the negative impact of social media-induced technostress is intensified when the job characteristics are low. This work extends the literature on job-stress, social media, technostress, and job characteristics.
Mining long-term search history to improve search accuracy
(2006)

Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.
Cobalt fill for advanced interconnects
(2017)

Cobalt as a material promises to change the conductor landscape in many areas, particularly logic contact and interconnect. In this paper, we focus on Cobalt as the conductor for logic interconnect — a potential Cu replacement. We demonstrate Co fill capability down to 10nm CD, and show that Co-Cu resistivity cross-over will occur below 14nm CD. Single damascene Co-ULK structures are used to establish an optimized metallization stack with robust CMP performance that has electromigration and TDDB reliability better than copper interconnect.
We're in it together: interpersonal management of disclosure in social network services
(2011)

The workload needed for managing privacy and publicness in current social network services (SNSs) is placed on individuals, yet people have few means to control what others disclose about them. This paper considers SNS-users' concerns in relation to online disclosure and the ways in which they cope with these both individually and collaboratively. While previous work has focused mainly on individual coping strategies, our findings from a qualitative study with 27 participants suggest that collaborative strategies in boundary regulation are of additional importance. We present a framework of strategies for boundary regulation that informs both theoretical work and design practice related to management of disclosure in SNSs. The framework considers disclosure as an interpersonal process of boundary regulation, in which people are dependent on what others choose to disclose about them. The paper concludes by proposing design solutions supportive of collaborative and preventive strategies in boundary regulation that facilitate the management of disclosure online.